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Control Systems, IEEE

Issue 2 • Date April 2010

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Displaying Results 1 - 25 of 48
  • [Front cover]

    Page(s): C1
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  • ECP [Educational Control Products - advertisement]

    Page(s): C2
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  • Omega Engineering

    Page(s): 1
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  • Table of contents

    Page(s): 2 - 4
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  • Quansar, Inc. [advertisement]

    Page(s): 3
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  • dSpace [advertisement]

    Page(s): 5
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  • The Tools That Jack Built [From the Editor]

    Page(s): 6
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  • A new era, a new ESC [ESC Silicon Valley 2010]

    Page(s): 7
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  • Quieter Clocks [About this Issue]

    Page(s): 8 - 10
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  • Research, a Never-Ending Story [President's Message]

    Page(s): 11 - 13
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  • 25 Years Ago

    Page(s): 14 - 103
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (298 KB) |  | HTML iconHTML  

    This article revisits some history of the IEEE Control Systems Society from the IEEE Centennial Lecture presented at the 1984 CDC conference meeting by Stephen Kahne in his article "Where Is the Beef?" (which originally appeared in IEEE Control Systems Magazine, vol. 5, no. 2, pp. 3-8, May 1985). It was in October 1954 that the Institute of Radio Engineers (IRE) started a Professional Group on Automatic Control (PGAC). Ehen the PGAC was started one of the main emphases was standards. In 1961, the American Institute of Electrical Engineers (AIEE) started the Technical Group on Automatic Control, which in fact was the first such technical group in the AIEE. The AIEE Professional Technical Group on Automatic Control was formed in 1963. In 1964, it changed its name to the Group on Automatic Control, and in 1971, to the Control Systems Society. View full abstract»

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  • IEEE Control Systems Magazine Board

    Page(s): 14
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  • Feedback

    Page(s): 15
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  • A Wrap-Up of 2009 Activities [Member Activities]

    Page(s): 16 - 109
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  • Technical Committee on Nonlinear Systems and Control [Technical Committee Activities]

    Page(s): 17 - 29
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  • Electrochemical Control of Lithium-Ion Batteries [Applications of Control]

    Page(s): 18 - 25
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1968 KB) |  | HTML iconHTML  

    While advanced batteries have enabled great improvements in society's mobility and energy efficiency, the high cost of batteries hampers further market penetration. Battery technology improvements are most often sought in electrochemical laboratories. But control engineers have an important role to play in advancing this technology. As a first step, the battery community must begin to merge the knowledge base of control theorists and practitioners with that of electrochemists and materials scientists. Robust integration of a battery into a high-power system is an experimentally burdensome process, with significant capital resources devoted to cycling batteries under a variety of power profiles and temperatures for several years to ensure reliability. A physics-based approach to battery integration offers the opportunity for streamlining control validation by setting physical limits that are accurate for all possible temperatures and operating scenarios. By introducing electrochemical state algorithms to existing Li-ion technology, usable power increases in the range of 20-50% seem possible for the entire family of Li-ion chemistries. Performance improvements may also be possible for Ni-MH chemistries with extensions to the method. In the end, improved accuracy and less conservative control limits mean more usable power and energy can be achieved for any given battery system. The outcome is that a smaller battery can provide the same capability, reducing both cost and weight. View full abstract»

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  • William Garrard [People in Control]

    Page(s): 26 - 27
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  • Asen Dontchev [People in Control]

    Page(s): 28 - 29
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  • Interference Estimation in IEEE 802.11 Networks

    Page(s): 30 - 43
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1468 KB)  

    This article describes a technique for distinguishing and quantifying medium access control (MAC) and physical layer (PHY) interference in error-prone 802.11 networks. This technique, is fully distributed, allowing each station to estimate interference individually. The estimator is based on an extended Kalman filter coupled to a mechanism for revealing abrupt changes in state. The network state is a vector of two components, representing PHY interference, expressed in terms of channel-error rate, and MAC interference. Two distinct state models are considered. When PHY interference can be assumed to be constant for all stations, network congestion is expressed by the number of competing terminals. View full abstract»

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  • Time and the Kalman Filter

    Page(s): 44 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1810 KB) |  | HTML iconHTML  

    This article reviews applications of the Kalman filter to atomic timing. The objectives of the article are twofold - present diverse applications and concepts in a consistent fashion, both in regard to notation and mathematical concepts. and develop the key ideas in a tutorial form by introducing the basic concepts and then applications.we apply the Kalman filter to clock estimation, clock monitoring, and time-scale definition. Furthermore, the GPS composite clock algorithm along with numerical simulations is described. Finally, the advantages and criticalities of the application of the Kalman filter to atomic timing, highlighting issues that are worth investigating are pointed out and on which the time and frequency community is currently working. View full abstract»

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  • Kalman Filtering in Wireless Sensor Networks

    Page(s): 66 - 86
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1884 KB)  

    Challenges associated with the scarcity of bandwidth and power in wireless communications have to be addressed. For the state-estimation problems discussed in the paper, observations about a common state are collected by physically distributed terminals. To perform state estimation, wireless sensor networks (WSNs) may share these observations with each other or communicate them to a fusion center for centralized processing. With K vector observations {yk(n)}K k=1 available, the optimal mean squared error (MSE) estimation of the state x(n) for the linear model is accomplished by a Kalman filter. View full abstract»

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  • Rudolf E. Kalman and His Students [Historical Perspectives]

    Page(s): 87 - 88
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  • A Tribute to Rudi Kalman [Historical Perspectives]

    Page(s): 88
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  • My Experiences with Prof. Rudy Kalman [Historical Perspectives]

    Page(s): 89
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  • My Stanford Days as a Graduate Student with R.E. Kalman [Historical Perspectives]

    Page(s): 89 - 90
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Aims & Scope

IEEE Control Systems Magazine is the largest circulation technical periodical worldwide devoted to all aspects of control systems.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Richard D. Braatz
braatz@mit.edu